Abstract

Migraine can only be detected by expert medical doctors. But recent studies showed that the migraine analysis can be done also by using EEG. These analyses are concerned with migraine diagnostic methods done by using EEG. T5-T3 channel of EEG was generally used in these proposed methods. However, the suitability of other channels in the diagnosis of migraine has not discussed. It is very important to find out which EEG channels and brain lobes are more important to learn the characteristics of migraine. The aim of this study is to analyze the each EEG channel separately for migraine patients. Analysis of this study is based on method in the literature that related to magnitude increase amount under flash stimulation. For this aim, beta band of each EEG channel’s data were pre-processed by using Burg-AR method. Then these features were applied to a support vector machine (SVM) classifier to observe which channel is the more definitive. As a result of this study, it is proposed that T3, F7, O1 and O2 channels are the most decisive for diagnosis of migraine, based on PSD magnitude increase under flash stimulation. Also, which brain lobes are more affected from triggering factors of migraine attack is proposed. Furthermore, asymmetry feature of migraine is approved by EEG and alternative migraine diagnosis methods is proposed for future researches according to reaction type of physiological structure of scalp to flash stimulation.

Keywords

EEG, Migraine, PSD, SVM.

Introduction

Migraine is one of the important brain disorders and its causes
have not known definitely yet [1,2]. Since, pain description of
patients is known as unique diagnosis method of migraine; its
diagnosis is a difficult task for a neurologist. Studies are
continuing for automatic diagnosis and determination of the
causes of it [3,4]. Electrical activity changes of brain during the
migraine attack were a critical point to determine the cause and
automatic diagnosis of migraine [5]. For this aim, the electrical
activity changes of brain obtained by triggering factors have
been usually used as a method. But practical migraine
diagnosis method approved by authority, has not been defined
yet. Although behavioural, environmental, infectious, dietary,
chemical, or hormonal factors are known as triggering factors
of migraine, cause of it hasn’t been known definitely [6].
Recent studies have proposed that EEG is the commonly used
tool for determining characteristics of migraine. By using EEG,
brain electrical activity changes obtained by triggering factors
for migraine patients, can be measured and studied for
migraine diagnosis easily. Flash stimulation was used as a
triggering method for activating migraine without aura in these
studies. Phase synchronization changes of alpha rhythm in
migraine patients under flash stimulation and revealing the existence of magnitude increasing in migraine patients under
flash stimulation are important examples of these diagnosis
methods by using EEG [7-9]. Since these former studies are
focused on T5-T3 channels of EEG to detect the migraine by
using computerized EEG diagnosis software, which EEG
channels (brain lobes) are more valuable has not been
determined completely yet. Therefore the electrical activity
change amount of each EEG channel under flash stimulation is
very important subject to define the cause of migraine and for
automatic diagnosis system.

It is reported that flash stimulated EEG data of migraine
patients at the beta band (13Hz-30Hz) of T5-T3 channels have
a PSD magnitude increase while healthy subjects haven’t any
magnitude changes in literature [7-11]. In this study, EEG data
is obtained from both migraine patients and healthy subjects
through different EEG channels under flash stimulation.

The flash stimulated and non-stimulated EEG signals of each
channel are filtered to obtain the beta band and pre-processed
by using Ar-burg method to obtain PSDs. PSD differences of
the pre-processed data as two classes (migraineurs and healthy
subjects) are used as the features that will be applied to a
support vector machine (SVM) classifier. As a result of this study it is determined that which brain lobes are more affected
from triggering factors (flash stimulation) of migraine and
which EEG channels have more importance on the automatic
diagnosis of migraine. Comparing the findings of the study, an
alternative migraine diagnosis method depending on the
physiological basis of face and scalp is proposed. Furthermore,
asymmetry feature of migraine is approved by EEG.

Data Acquisition

Subjects

Migraineurs and healthy subject’s EEG data is obtained from
Neurology Department of Kahramanmaras Sutcu Imam
University. Migraineurs (without aura) group consisted of
thirty patients (nine males, twenty one females), and were
diagnosed according to the diagnostic criteria proposed by the
International Headache Society (IHS). Control group (healthy
subjects) consisted of thirty healthy subjects (eleven males,
nineteen females) which has not any neurological or
psychiatric disease. Both healthy and migraineur groups’ age
ranges are between 20-40 years. Subjects (migraineurs and
healthy subjects) had not taken any drug before the recordings
and all were in the interictal (pain-free) state. They were tested
in a dimly lit room, while in a couchant position. All the
subjects were instructed to relax during the experiment,
keeping their eyes closed.

Data recording

EEG recordings were obtained with an 18-Channel Nicolet
One Machine. Electrodes were positioned according to the
international 10–20 system (Figure 4b), at Fp1, Fp2, F7, F3,
Fz, F4, F8, T3, C3, C4, T4, T5, P3, Pz, P4, T6, O1 and O2.
The reference electrode was positioned at the linked earlobes
(A1-A2) and the EEG signals were sampled at a rate of 256
Hz. Each recording process was taken minimum 20 minutes
that contain 30s hyperventilation and 30s flash stimulation
periods. In these 30 seconds flash stimulation time periods;
stimulation frequency was 2, 4 and 6 Hz. Since the best
definitive stimulation frequency is reported as 4 Hz in previous
study [7], this stimulation frequency of 4 Hz is used. Sample
EEG signals are given in figure 1.

Figure 1: Sample EEG Signals.

Methods

Pre-processing of EEG signals

Feature extraction or sample reduction of EEG signals is a
crucial step that influences the performance of the classifier. In
this study, the beta band of EEG data is preprocessed to get the
low-dimensional feature vectors that are frequency-magnitude
relations. Power spectral density (PSD) of EEG data is
obtained by using Burg-Ar method (Figure 2). This method is a
model-based (parametric) method which models the data
sequencex(n) as the output of a linear system characterized by a
rational structure. In the model-based methods, for estimating
the spectrum firstly parameters are estimated from a given data
sequencex(n), 0 ≤ n ≤ N-1. Afterwards, by using these
estimates, the power spectral density estimate can be
computed. Details can be found in literature [11-13].

Figure 2: Block diagram of this study.

One of the better known criteria for selecting the model order
has been proposed by Akaike called the Akaike information
criterion (AIC) [13]. In this study, our selected model order of
the AR method was 10 by using AIC. The differences between
flash-stimulated and non-stimulated EEG PSDs are calculated
as the last step of the pre-processing. These PSD differences
are used as the feature vectors to be applied to the
classification step as seen in figure 2.

In this study the noise and artifact removal algorithms were not
used since the EEG recordings made and used by experts are
the mostly raw data. Also, a lot of pre-processing and
classification methods have tried for this study but could not
find better than the proposed method.

Support vector machines

Support vector machines (SVMs) are well-known supervised
learning methods that were developed by C. Cortes and V.
Vapnik for binary classification and regression [14-16]. SVMs
classify data in two steps: first, given a set of training
examples, each marked as belonging to one of two categories
by hyperplane. The hyperplane is a classifier which leaving the
largest possible fraction of points of the same class on the same
side, while maximizing the distance of either class from the
hyperplane. It is determined by a subset of the points of the
two classes, named as support vectors. Then SVM training
algorithm builds a model by assistance of these two categories
separated by hyperplane that predicts whether a new example falls into one category or the other according to positions of
hyperplane [16-18].

In this study, linear support vector machines are used to
classify the EEG data for migraine analysis. Differences
between PSDs of flash-stimulated and non-stimulated data
were used as inputs for SVM classifier. 90% of overall data
were used for training and the rest of the data were used for
testing. To obtain an objective classification accuracy rate 10-
fold cross validation was used.

Performance measures

The aim of the SVM classification is to assign the input
patterns to one of the two classes, usually represented by
outputs restricted to lie in the range from 0 to 1 (0:Normal,
1:Migrainuer), so that they represent the probability of class
membership. After classification step, the classification
performance of data is evaluated by using sensitivity,
specificity and accuracy measures. Terms of sensitivity,
specificity and accuracy values are formulated as below [17]:

TP = True Positive, FN = False Negative,

TN = True Negative, FP = False Positive

Also Cohen's kappa coefficients are calculated to analyze the
EEG data. The Cohen's kappa coefficient is a statistical method
that measures the reliability of the agreement between two
raters. This method determines if there is an agreement
between two raters by chance with a percentage [19].
Implementation of the method is as follows: If the predictions
of raters are determined as PA and PB.

The value of Kappa is defined as:

Where,

And

Finally;

Kappa values are easily interpreted for the following:

K=1: Raters fully complies with each other

K=0: Compliance for raters determined only by chance (There
is no agreement).

The other values can be interpreted with the following table
(Landis et al. [20])

Results

The SVM classification achievements of each channel are
given in table 3. According to these results, one can see that
accuracy rates change between 55.7% and 88.4% depending on
the EEG channels. By examining the table it can also be seen
that the best classifiable data were from T3, F7, O1 and O2
channels which have comparatively higher accuracy rates
between 81.8% and 88.4% respectively. As a consequence, we
can say that the data from T3, F7, O1 and O2 channels exhibits
considerably better performance than other channels.

Second Rater Predictions

First Rater Predictions

Prediction of A Value
(percentage)

Prediction of B Value
(percentage)

Prediction of A Value
(percentage)

PAA

PAB

Prediction of B Value
(percentage)

PBA

PBB

Table 1: Calculation of Kappa values.

Interpretation

Poor agreement

Fair agreement

Moderate agreement

Good agreement

Very good agreement

K Value

<0.2

0.2-0.4

0.4-0.6

0.6-0.8

0.8-1

Table 2: Interpretation of Kappa values.

Left Lobe

Accuracy %

Sensitivity %

Specificity %

Fp1

68.7

70.7

66.7

F7

85

90

80

T3

88.4

90

86.7

T5

72.2

64.3

80

O1

81.8

83.6

80

P3

65.7

51.4

80

C3

69.1

51.4

86.7

F3

68.8

64.3

73.3

Fz

68.8

64.3

73.3

Right Lobe

Accuracy %

Sensitivity %

Specificity %

Fp2

55.7

51.4

60

F8

62.4

51.4

73.3

T4

65.6

57.9

73.3

T6

65.6

57.9

73.3

O2

81.8

83.6

80

P4

65.4

70.7

60

C4

59.1

51.4

66.7

F4

68.8

64.3

73.3

Pz

68.8

64.3

73.3

Table 3: SVM accuracy rates of EEG channels for migraine diagnosis.

To see whether these results are true or not by chance, we
applied the classification results to kappa test. Kappa results are given in table 4 that satisfies the classification results with
the higher kappa values. T3, F7, O1 and O2 channels have
comparatively higher kappa values between 0.64 and 0.77
respectively. Regarding the accuracy rates and kappa values, it
can be concluded that T3, F7, O1 and O2 channels are more
helpful or dominant for the migraine analysis.

Fp1

F7

T3

T5

O1

P3

C3

F3

Fz

Kappa

0.45

0.70

0.77

0.44

0.64

0.31

0.38

0.37

0.37

Fp2

F8

T4

T6

O2

P4

C4

F4

Pz

Kappa

0.11

0.24

0.31

0.31

0.64

0.31

0.18

0.37

0.37

Table 4: Kappa values of EEG channels for migraine diagnosis.

Discussion

Migraine is a common neurological disorder that is not known
its causes definitely yet. Since, the diagnosis of migraine is a
difficult task for a neurologist, automatic diagnosis and
determination of it has a great importance. In this study, flash-stimulated
and non-stimulated EEG signals are used to
diagnose or analyse migraine without aura. It often begins with
a dull ache and then develops into a constant, throbbing and
pulsating pain that you may feel especially at the superficial
temporal artery, as well as the occipital artery of the head
[21,22]. It is also reported that during the migraine attack facial
arteries of head, shrink or dilate [23].

In human anatomy, auditory cortex is placed in the temporal
lobe and visual cortex is placed in the occipital lobe of brain
(Figure 3c). The superficial temporal artery is the major artery
of the head. It begins in the substance of the parotid gland,
behind the neck of the mandible, and passes superficially over
the posterior root of the zygomatic (cheekbone) process of the
temporal bone; about 5 cm. Above this process it is subdivided
into two branches (frontal and parietal) [26]. The mean
diameter of the temporal artery at the zygomatic arch was
determined as 2.73 ± 0.51 mm. The diameters of the frontal
branch were bigger than those of the parietal branch [27]. The second major artery of head is occipital artery. The occipital
artery arises from the external carotid artery opposite the facial
artery; its path is below the posterior belly of digastric (Small
muscle located under the jaw) to the occipital region. Diameter
of occipital artery is smaller than temporal artery [28]. Arteries
of face, scalp and brain lobes are shown in figure 3.

If these physiological realities are examined together with the
results of this study (Table 3 and Table 4) one can see a good
consistency between them. The highest classification accuracy
rate (88.7%) and kappa value (0.77%) were obtained with the
T3 channel of the EEG data which is positioned over the temporal artery. F7 electrode is positioned over the frontal
branch of temporal artery has the second best accuracy rate
(0.85) and kappa value (0.70) according to SVM results. O1
and O2 channels recorded over occipital artery have the third
(slightly less) accuracy rate (81.8%) and kappa value (0.64).
According to analysis results, other EEG channels have less
accuracy rates (≤ 69.1%) and kappa values (≤ 0.45) than that of
T3, F7, O1 and O2 channels. These channels are placed on the
auditory cortex and visual cortex. Also they are positioned over
thinner diameter of vessels proportion to temporal and occipital
artery except F8 and T4 channels.

Another critical point that was shown from SVM results is that;
although temporal arteries are existed on both sides of head, T3
and F7 channels which have the highest accuracies are
positioned left side of head over the temporal artery and frontal
branch. If so, it can be said that the asymmetry feature of
migraine is approved by using EEG. According to these results
it can be proposed that EEG data recorded from T3, F7, O1 and
O2 channels are more definitive for diagnosis of migraine
without aura. In addition, it is observed that; for migraines left
side of head is more affected from triggering factor (flash
stimulation) than right side of head.

After the pre-processing and classification steps, it can be
concluded that; measuring the T3, F7, O1 and O2 channels of
EEG are sufficient for diagnosis of migraine. If the obtained
results are examined, one can see that these channels are
positioned over the visual and auditory cortex of brain.
Crackling sound and repetitive light of flash stimulation
process may stimulate these cortexes. It is also known that
sound and light are triggering factors of migraine; obtained
results are relevant with this physiological structure of the
brain. Since, flash stimulation affects these cortexes; they may
need more blood during the flash stimulation that can be
studied in future by measuring blood flow of arteries.